Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021011
S. Populaire, Joëlle Blanc, Thierry Denœux, Philippe Ginestet
This paper presents a methodology for combining expert knowledge with information from statistical data, in classification and prediction problems. The method is based on (1) a case-based approach allowing to predict a quantity of interest from past cases in the form of a belief function, (2) Bayesian networks for modelling expert knowledge and (3) a tuning mechanism allowing to optimally discount information sources by optimizing a performance criterion. This methodology is applied to the prediction of chemical oxygen demand solubility in waste-water The approach is expected to be useful in situations where both small databases and partial expert knowledge are available.
{"title":"Fusion of expert knowledge with data using belief functions: a case study in waste-water treatment","authors":"S. Populaire, Joëlle Blanc, Thierry Denœux, Philippe Ginestet","doi":"10.1109/ICIF.2002.1021011","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021011","url":null,"abstract":"This paper presents a methodology for combining expert knowledge with information from statistical data, in classification and prediction problems. The method is based on (1) a case-based approach allowing to predict a quantity of interest from past cases in the form of a belief function, (2) Bayesian networks for modelling expert knowledge and (3) a tuning mechanism allowing to optimally discount information sources by optimizing a performance criterion. This methodology is applied to the prediction of chemical oxygen demand solubility in waste-water The approach is expected to be useful in situations where both small databases and partial expert knowledge are available.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114577808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020933
Daniel G. Schwartz, S. Stoecklin, E. Yilmaz
This paper reports progress on creating a case-based implementation of the well-known Snort intrusion detection system. Snort is a simple rule-based system that is known to suffer limitations, including both failure to detect certain kinds of intrusions and the frequent raising of false alarms. We believe that a case-based reasoning approach can provide a framework in which to incorporate more sophisticated artificial intelligence techniques that will help overcome some of these limitations. In addition, the present system is intended to apply more generally to other aspects of network security, as well as other domains related to protecting the nation's critical infrastructure. The system is being built using the modern software engineering technique known as "adaptive" or "reflective architectures," which will make it easily adaptable to other kinds of problem domain.
{"title":"A case-based approach to network intrusion detection","authors":"Daniel G. Schwartz, S. Stoecklin, E. Yilmaz","doi":"10.1109/ICIF.2002.1020933","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020933","url":null,"abstract":"This paper reports progress on creating a case-based implementation of the well-known Snort intrusion detection system. Snort is a simple rule-based system that is known to suffer limitations, including both failure to detect certain kinds of intrusions and the frequent raising of false alarms. We believe that a case-based reasoning approach can provide a framework in which to incorporate more sophisticated artificial intelligence techniques that will help overcome some of these limitations. In addition, the present system is intended to apply more generally to other aspects of network security, as well as other domains related to protecting the nation's critical infrastructure. The system is being built using the modern software engineering technique known as \"adaptive\" or \"reflective architectures,\" which will make it easily adaptable to other kinds of problem domain.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"708 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115125764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020910
Subhash Challa, Jonathan A. Legg
Fusing out-of-sequence information is a problem of growing importance due to an increased reliance on networked sensors embedded in complicated network architectures. The problem of fusing out-of-sequence measurements (OOSM) has received some attention in literature; however, most practical fusion systems, owing to compatibility with legacy sensors and limited communication bandwidth, send track information instead of raw measurements to the fusion node. Delays introduced by the network can result in the reception of out-of-sequence tracks (OOST). This paper considers the problem of fusing out-of-sequence measurements in general, and proposes an optimal Bayesian solution involving a joint probability density of current and past target states, referred to as augmented states. By representing tracks using equivalent measurements, the relationship between OOSM and OOST-based fusion is shown. The special case of Gaussian statistics is also addressed.
{"title":"Track-to-track fusion of out-of-sequence tracks","authors":"Subhash Challa, Jonathan A. Legg","doi":"10.1109/ICIF.2002.1020910","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020910","url":null,"abstract":"Fusing out-of-sequence information is a problem of growing importance due to an increased reliance on networked sensors embedded in complicated network architectures. The problem of fusing out-of-sequence measurements (OOSM) has received some attention in literature; however, most practical fusion systems, owing to compatibility with legacy sensors and limited communication bandwidth, send track information instead of raw measurements to the fusion node. Delays introduced by the network can result in the reception of out-of-sequence tracks (OOST). This paper considers the problem of fusing out-of-sequence measurements in general, and proposes an optimal Bayesian solution involving a joint probability density of current and past target states, referred to as augmented states. By representing tracks using equivalent measurements, the relationship between OOSM and OOST-based fusion is shown. The special case of Gaussian statistics is also addressed.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124668903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021170
S. Wellington, J.D. Vincent
The Nadaraya-Watson (N-W) statistical estimator based on Haar kernels can be used to implement a fuser based on empirical data. Fuser design essentially consists of the following interrelated activities: select a set of n observations from a pool of p prior observations; select a value for the bandwidth. Optimal fuser design can therefore involve a very large search space. This paper proposes the use of a genetic algorithm (GA) to optimise the fuser design. The GA is used to evolve optimal values for the bandwidth and subset of observations used to implement the fuser. Indicative test results are provided. The N-W fuser is shown to perform better than the best single sensor. The GA provides better results than manual design optimisation, with the performance of the N-W fuser comparable to that achieved using a feedforward neural network.
{"title":"Design optimisation of the Nadaraya-Watson fuser using a genetic algorithm","authors":"S. Wellington, J.D. Vincent","doi":"10.1109/ICIF.2002.1021170","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021170","url":null,"abstract":"The Nadaraya-Watson (N-W) statistical estimator based on Haar kernels can be used to implement a fuser based on empirical data. Fuser design essentially consists of the following interrelated activities: select a set of n observations from a pool of p prior observations; select a value for the bandwidth. Optimal fuser design can therefore involve a very large search space. This paper proposes the use of a genetic algorithm (GA) to optimise the fuser design. The GA is used to evolve optimal values for the bandwidth and subset of observations used to implement the fuser. Indicative test results are provided. The N-W fuser is shown to perform better than the best single sensor. The GA provides better results than manual design optimisation, with the performance of the N-W fuser comparable to that achieved using a feedforward neural network.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123534262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021230
P. Ainsleigh, T. Luginbuhl
The probabilistic multi-hypothesis tracking (PMHT) algorithm is extended for application to classification. The PMHT model is reformulated as a bank of continuous-state hidden Markov models, allowing for supervised learning of the class-conditional probability density models, and for likelihood evaluation of multicomponent signals.
{"title":"Multicomponent signal classification using the PMHT algorithm","authors":"P. Ainsleigh, T. Luginbuhl","doi":"10.1109/ICIF.2002.1021230","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021230","url":null,"abstract":"The probabilistic multi-hypothesis tracking (PMHT) algorithm is extended for application to classification. The PMHT model is reformulated as a bank of continuous-state hidden Markov models, allowing for supervised learning of the class-conditional probability density models, and for likelihood evaluation of multicomponent signals.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121738525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021193
W. P. Malcolm, A. Doucet, S. Zollo
In this article we consider tracking a single maneuvering target in scenarios where range information is not available, or is denied. This tracking problem is usually referred to as passive ranging, or bearings-only tracking. Tracking any single maneuvering target naturally admits a jump Markov system, in which a collection of candidate dynamical systems is proposed to model various classes of motion, each of which is assumed to be executed by the target according to a Markov law. Standard techniques to solve this problem use the so called interacting multiple model (IMM), or its variants. Recently sequential Monte Carlo (SMC) techniques have been applied to passive ranging problems, however, most of the scenarios reported in the literature consider nonmaneuvering targets. In this article we apply a new SMC technique to the passive ranging problem in a maneuvering target scenario. The algorithm we propose is compared to the so called auxiliary particle filter (APF). A simulation study is included.
{"title":"Sequential Monte Carlo tracking schemes for maneuvering targets with passive ranging","authors":"W. P. Malcolm, A. Doucet, S. Zollo","doi":"10.1109/ICIF.2002.1021193","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021193","url":null,"abstract":"In this article we consider tracking a single maneuvering target in scenarios where range information is not available, or is denied. This tracking problem is usually referred to as passive ranging, or bearings-only tracking. Tracking any single maneuvering target naturally admits a jump Markov system, in which a collection of candidate dynamical systems is proposed to model various classes of motion, each of which is assumed to be executed by the target according to a Markov law. Standard techniques to solve this problem use the so called interacting multiple model (IMM), or its variants. Recently sequential Monte Carlo (SMC) techniques have been applied to passive ranging problems, however, most of the scenarios reported in the literature consider nonmaneuvering targets. In this article we apply a new SMC technique to the passive ranging problem in a maneuvering target scenario. The algorithm we propose is compared to the so called auxiliary particle filter (APF). A simulation study is included.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126181811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021009
Wang Ming-Hui, Peng Ying-ning, You Zhi-sheng
The joint probabilistic data association (JPDA) filter has a very good tracking performance in dense targets and heavy clutter environments. However, the JPDA filter also has a huge computer load and tends to combine neighboring tracks. In this paper, an improved JPDA algorithm is presented. The main feature of our method is improving the performance of the JPDA algorithm by improving the performance of the tracking gate. The effectiveness of this method is assessed by mathematical analysis.
{"title":"Improved joint probabilistic data association algorithm","authors":"Wang Ming-Hui, Peng Ying-ning, You Zhi-sheng","doi":"10.1109/ICIF.2002.1021009","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021009","url":null,"abstract":"The joint probabilistic data association (JPDA) filter has a very good tracking performance in dense targets and heavy clutter environments. However, the JPDA filter also has a huge computer load and tends to combine neighboring tracks. In this paper, an improved JPDA algorithm is presented. The main feature of our method is improving the performance of the JPDA algorithm by improving the performance of the tracking gate. The effectiveness of this method is assessed by mathematical analysis.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129853333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020891
Dongguang Zuo, Chongzhao Han, Zheng Lin, Hongyan Zhu, Han Hong
This paper develops a tracking algorithm for maneuvering target based on fuzzy logic inference (FMMTA). In place of the model probability computed intricately in the IMM, filtering measurement innovations are tackled with the innovation covariance, and the results are used as the input to a fuzzy inference system to get the matched degrees for each filtering model in the model set designed. With the matched degrees, the estimation from each filtering is weighted to obtain the maneuvering target's overall estimation and its covariance. The performance of FMMTA is tested via Monte Carlo simulation, and the result expresses its validity and its promise.
{"title":"Fuzzy multiple model tracking algorithm for manoeuvring target","authors":"Dongguang Zuo, Chongzhao Han, Zheng Lin, Hongyan Zhu, Han Hong","doi":"10.1109/ICIF.2002.1020891","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020891","url":null,"abstract":"This paper develops a tracking algorithm for maneuvering target based on fuzzy logic inference (FMMTA). In place of the model probability computed intricately in the IMM, filtering measurement innovations are tackled with the innovation covariance, and the results are used as the input to a fuzzy inference system to get the matched degrees for each filtering model in the model set designed. With the matched degrees, the estimation from each filtering is weighted to obtain the maneuvering target's overall estimation and its covariance. The performance of FMMTA is tested via Monte Carlo simulation, and the result expresses its validity and its promise.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"01 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129880387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021144
J. R. Hoffman, R. Mahler
The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-sensor, single-target systems. One might expect that multisensor, multitarget information fusion theory and applications would already rest upon a similarly fundamental concept-namely, miss distance between multi-object systems (i.e., systems in which not only individual objects can vary, but their number as well). However, this has not been the case. Consequently, in this paper we introduce a comprehensive theory of distance metrics for multitarget (and, more generally, multi-object) systems. We show that this theory extends an optimal-assignment approach proposed by O. Drummond. We describe tractable computational approaches for computing such metrics, as well as some potentially far-reaching implications for applications such as sensor management.
{"title":"Multitarget miss distance and its applications","authors":"J. R. Hoffman, R. Mahler","doi":"10.1109/ICIF.2002.1021144","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021144","url":null,"abstract":"The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-sensor, single-target systems. One might expect that multisensor, multitarget information fusion theory and applications would already rest upon a similarly fundamental concept-namely, miss distance between multi-object systems (i.e., systems in which not only individual objects can vary, but their number as well). However, this has not been the case. Consequently, in this paper we introduce a comprehensive theory of distance metrics for multitarget (and, more generally, multi-object) systems. We show that this theory extends an optimal-assignment approach proposed by O. Drummond. We describe tractable computational approaches for computing such metrics, as well as some potentially far-reaching implications for applications such as sensor management.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"496 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129278782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020934
Richard Brooks, Nathan Orr, John Zachary, Christopher Griffin
Malicious network activity is rapidly increasing. To understand and engineer countermeasures to network attacks, we have developed cellular automata models of network flow dynamics and associated attacks. We describe the theoretical development of our model and compare it to existing models of network flow based on statistical physics. Using our model, we have found empirical evidence that a link exists between the behavior of a network and its entropy. This paper discusses potential extensions of this work to entropy-based intrusion detection systems (IDS).
{"title":"An interacting automata model for network protection","authors":"Richard Brooks, Nathan Orr, John Zachary, Christopher Griffin","doi":"10.1109/ICIF.2002.1020934","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020934","url":null,"abstract":"Malicious network activity is rapidly increasing. To understand and engineer countermeasures to network attacks, we have developed cellular automata models of network flow dynamics and associated attacks. We describe the theoretical development of our model and compare it to existing models of network flow based on statistical physics. Using our model, we have found empirical evidence that a link exists between the behavior of a network and its entropy. This paper discusses potential extensions of this work to entropy-based intrusion detection systems (IDS).","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116005266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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